activity
20212023
most citedDiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

39 citations · 54 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2023★ 2 cited

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Youquan Liu, Runnan Chen, Xin Li +9

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a n…

cs.RO2023★ 39 cited

DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

Licheng Wen, Daocheng Fu, Xin Li +7

Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretab…

cs.CV2023★ 2 cited

DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds

Tao Ma, Xuemeng Yang, Hongbin Zhou +9

Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D…

cs.CV2023★ 5 cited

LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

Xin Li, Tao Ma, Yuenan Hou +8

LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and poi…

cs.CV2022★ 2 cited

Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection

Xin Li, Botian Shi, Yuenan Hou +4

Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D…

cs.CV2022

Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

Keli Huang, Botian Shi, Xiang Li +3

Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good perform…

cs.CV2022★ 1 cited

Cross-Domain Document Layout Analysis Using Document Style Guide

Xingjiao Wu, Luwei Xiao, Xiangcheng Du +5

The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with st…

cs.CV2021★ 2 cited

Document Layout Analysis with Aesthetic-Guided Image Augmentation

Tianlong Ma, Xingjiao Wu, Xin Li +4

Document layout analysis (DLA) plays an important role in information extraction and document understanding. At present, document layout analysis has reached a milestone achievemen…

cs.CV2021★ 1 cited

Human-In-The-Loop Document Layout Analysis

Xingjiao Wu, Tianlong Ma, Xin Li +2

Document layout analysis (DLA) aims to divide a document image into different types of regions. DLA plays an important role in the document content understanding and information ex…